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Financial signal processing is a branch of signal processing technologies which applies to financial signals. They are often used by quantitative investors to make best estimation of the movement of equity prices, such as stock prices, options prices, or other types of derivatives.


The early history of financial signal processing can be traced back to Isaac Newton. Newton lost money in the famous South Sea Company investment bubble.

The modern start of financial signal processing is often credited to Claude Shannon. Shannon was the inventor of modern communication theory. He discovered the capacity of a communication channel by analyzing entropy of information.[1]

For a long time, financial signal processing technologies have been used by different hedge funds, such as Jim Simon's Renaissance Technologies. However, hedge funds usually do not reveal their trade secrets. Some early research results in this area are summarized by R.H. Tütüncü and M. Koenig[2] and by T.M. Cover, J.A. Thomas.[3] A.N. Akansu and M.U. Torun published the book in financial signal processing entitled A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading.[4] An edited volume on the subject with the title Financial Signal Processing and Machine Learning was also published.[5] There were two special issues of IEEE Journal of Selected Topics in Signal Processing published on Signal Processing Methods in Finance and Electronic Trading in 2012,[6] and on Financial Signal Processing and Machine Learning for Electronic Trading in 2016[7] in addition to the special section on Signal Processing for Financial Applications in IEEE Signal Processing Magazine appeared in 2011.[8]

Imperial College London Financial Signal Processing LaboratoryEdit

Recently, a new research group in Imperial College London has been formed which focuses on Financial Signal Processing as part of the Communication and Signal Processing Group of the Electrical and Electronic Engineering department,[9] led by Anthony G. Constantinides. In June 2014 the group started a collaboration with the Schroders Multi-Asset Investments and Portfolio Solutions (MAPS) team on multi-asset study.[10]


  1. ^ "Connections Between Financial Signal Processing, Entropy, and Superior Investment Returns, James Simon, Jim Simon, Renaissance Technologies". Retrieved 2013-06-16.
  2. ^ Tütüncü, Reha H. and Koenig, Mark, "Robust asset allocation", Annals of Operations Research, vol. 132, pp. 157–187, 2004
  3. ^ Cover, Thomas M. and Thomas, Joy A., Elements of Information Theory, 2nd Edition, Wiley, 2006
  4. ^ Akansu, Ali N.; Torun, Mustafa U., A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading, Boston, MA: Academic Press, 2015 ISBN 978-0-12-801561-2
  5. ^ Akansu, Ali N.; Kulkarni, Sanjeev R.; Malioutov, Dmitry M., Eds., Financial Signal Processing and Machine Learning, Hoboken, NJ: Wiley-IEEE Press, 2016 ISBN 978-1-118-74567-0
  6. ^ "IEEE Xplore: IEEE Journal of Selected Topics in Signal Processing - (Volume 6 Issue 4)".
  7. ^ "IEEE Xplore: IEEE Journal of Selected Topics in Signal Processing - (Volume 10 Issue 6)".
  8. ^ "IEEE Xplore: IEEE Signal Processing Magazine - (Volume 28 Issue 5)".
  9. ^ "Financial Signal Processing Lab". Retrieved 2014-02-17.
  10. ^ "Schroders Press Release". Retrieved 2014-07-15.